Metric learning [1]} aims to measure the distance among samples while using an optimal distance metric for learning tasks. Instead of providing an explicit label, metric learning compares the distance among inputs to construct a metric space. In the last few years, deep learning and metric learning have been brought together to introduce the concept of deep metric learning [2]}. Most of the existing deep metric learning methods can be roughly categorized based on loss functions: 1) contrastive loss [3]}; 2) triplet loss [4]}; 3) margin ranking loss [5]}, [6]}. Contrastive loss and triplet loss have a similar formulation to close the distance between semantic-similar samples and enlarge the distance between dissimilar samples. Similar to the hinge loss in support vector machine (SVM) [7]}, margin ranking loss is designed for capturing the ranking relationship between input training samples.
In this paper, we utilize the margin ranking loss to construct the metric space of degradation levels, which drives our model to predict degradation levels in an unsupervised manner.
<FIGURE>